Categorical Data Visualization - Problem Set 6

Objectives

• The objective of this assignment is to conduct an exploratory data analysis of a data set that you are not familiar with. In this week’s lecture, we discussed a number of visualization approaches in order to explore a data set with categorical variables. This assignment will apply those tools and techniques. An important distinction between class examples and applied data science work is iterative and repetitive nature of exploring a data set. It takes time to understand what the data is and what is interesting about the data (patterns).

• For this week, we will be exploring the Copenhagen Housing Conditions Survey:

  • Sat - satisfaction of householders with their present housing circumstances
  • Infl - perceived degree of influence householders have on the management of the property
  • Type - type of rental accomodation
  • Cont - contact residents are afforded with other residents
  • Freq - Frequencies: the numbers of residents in each class

• Your task for this assignment is to use ggplot and the facet_grid and facet_wrap functions to explore the Copenhagen Housing Conditions Survey. Your objective is to develop 5 report quality visualizations (at least 4 of the visualizations should use the facet_wrap or facet_grid functions) and identify interesting patterns and trends within the data that may be indicative of large scale trends. For each visualization you need to write a few sentences about the trends and patterns that you discover from the visualization.

To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyperlinked and that I can see the visualization and the code required to create it.

Look at the data

str(housing)

## 'data.frame':    72 obs. of  5 variables:
##  $ Sat : Ord.factor w/ 3 levels "Low"<"Medium"<..: 1 2 3 1 2 3 1 2 3 1 ...
##  $ Infl: Factor w/ 3 levels "Low","Medium",..: 1 1 1 2 2 2 3 3 3 1 ...
##  $ Type: Factor w/ 4 levels "Tower","Apartment",..: 1 1 1 1 1 1 1 1 1 2 ...
##  $ Cont: Factor w/ 2 levels "Low","High": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Freq: int  21 21 28 34 22 36 10 11 36 61 ...

1. First plot

Number of Resident by Satisfaction with Rental Type. Apartment residents with high contact alos have high satisfaction.

ggplot(housing) +
geom_bar(aes(x = Cont, y = Freq, fill = Sat), stat = 'identity', width =.4, position = "dodge") +
  labs(title="Number of Resident by Satisfaction with Rental Type ") + 
  labs(fill = "Satisfaction") +
  xlab("Contact") + 
  ylab("Number of Resident") +
  facet_wrap(~Type)

2. Second plot

number of residents with different levels of perceived degree of influence on the management of the property by 4 types of rental property, by amount of cost shared with other residents

ggplot(housing, aes(x = Infl, y = Freq))+
  geom_bar(
    aes(fill = Cont), stat = "identity", color = "gray",
    position = position_dodge(0.8)
    )+
  facet_wrap(~Type) 

3. Third plot

Residents in apartments with low satisfaction and influence have the highest frequency. It can be said that more people are unhappy that the management has less influence

p4<-ggplot(housing, aes(x=Infl, y=Freq)) + geom_point(shape=1)
p4+facet_grid(Type~Sat)

4. Fourth plot

Residents in Apartment and Terrace have a higher contact with other residents

p2<-ggplot(housing, aes(x=Cont, y=Freq)) + geom_point(shape=1)
p2+facet_wrap(~Type,ncol=2)

5. Fifth plot

Residents with higher satisfaction tend to have higher contact with other residents.

ggplot(housing, aes(x=Sat, y=Freq)) + geom_point(shape=1)+
facet_grid(Cont~.)